{
 "metadata": {
  "name": "Intro to Pandas"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import pandas as pd\nts = pd.read_csv('Gold.csv', index_col=0, parse_dates=True)\nts",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<pre>\n&ltclass 'pandas.core.frame.DataFrame'&gt\nDatetimeIndex: 125 entries, 2003-01-31 00:00:00 to 2013-05-31 00:00:00\nData columns (total 1 columns):\nprice    125  non-null values\ndtypes: float64(1)\n</pre>",
       "output_type": "pyout",
       "prompt_number": 1,
       "text": "<class 'pandas.core.frame.DataFrame'>\nDatetimeIndex: 125 entries, 2003-01-31 00:00:00 to 2013-05-31 00:00:00\nData columns (total 1 columns):\nprice    125  non-null values\ndtypes: float64(1)"
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ts.plot()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 2,
       "text": "<matplotlib.axes.AxesSubplot at 0x3a5f810>"
      },
      {
       "output_type": "display_data",
       "png": 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ICAiAj48PYmNjG+/cQth6btLW9eXgQWp9OXiwZf3Ll8U4ek9P/fYuXUQPuzY6dKgM9Npi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\n4UDPMAxj53CgZxiGsXP+H35/E4ny+8qYAAAAAElFTkSuQmCC\n",
       "text": "<matplotlib.figure.Figure at 0x3a5f5d0>"
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ts[\"2006\":\"2007\"].plot(color = \"green\")",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 3,
       "text": "<matplotlib.axes.AxesSubplot at 0x4464750>"
      },
      {
       "output_type": "display_data",
       "png": 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Hhy528vl88Pl8jB49GgDg4+OD69evQ0dHBzweDzweD4GBgeLyib6+PnJzc8Xb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\n",
       "text": "<matplotlib.figure.Figure at 0x44489d0>"
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ts_res = ts.resample(\"A\")\nts_res.plot(style='g--')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 4,
       "text": "<matplotlib.axes.AxesSubplot at 0x42d8290>"
      },
      {
       "output_type": "display_data",
       "png": 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l7CjbIQcLos1rUoMPDg5m8+bNnDp1CqUU3377LSaTiaioKNLT0wFIT08nJiYG\ngOjoaDIyMrBYLBQVFVFYWEhYWJj99sKOLn4b2pa5YhZnq88y+9vZRH8Yjbmq7jFgU7hiFi1FsrDR\nehZNmsEPHDiQyZMnc8MNN+Dm5sb111/PAw88QFVVFbGxsSxatMh6miSAyWQiNjYWk8mEu7s7qamp\n6HQ6u+6IEEWVRUz8fCLe7b3Z8eAOuv1/3ZxdkhBOJdeiEa3Cp3mfMu2raTz5xyd59MZHcdPJd/hE\n2yHXgxetVnVNNRn/yeCriV8xRD/E2eUIoRlymHMRrc/UHMlVsmjn1o5PYz9t0ebuKlk4gmRho/Us\npMELIUQrJTN44VKOnj4KQOf2nZ1ciRDaIdeDFy5vq3kr1y+8nk/zPnV2KUK4BGnwF9H6TM2RtJJF\njarhlY2vcPsHtzN31FymXj/V4TVoJQstkCxstJ6FnEUjNO3XE7+SsCyBytOVbLl/C0Yvo7NLEsJl\nyAxeaNrf1/6d6ppq/t+I/4dHOw9nlyOEJtn1evAtRRq8uJhSSr71LMRlyIesV0jrMzVHckQWlmoL\nX+R/wV+z/lrn7Vpp7vK6sJEsbLSehczghVPsLt/N4tzF/HvXv+nbtS9TQqdQo2rkEgNC2JGMaITD\nxX4Sy6aSTcQPjCchNIFAn0BnlySES5MZvNCMfUf24e/pTzu3ds4uRYhWQWbwV0jrMzVHak4WP1f8\nTPa+uh9v9DK6XHOX14WNZGGj9SykwQu7OW45TlpuGsPThnPTopvYat7q7JKEaNNkRCOa7cy5M0z7\nehqf53/OzdfdTGJoIrf3vp2r2l3l7NKEaBNkBi9a1OKdi4kMjKR7p+7OLkWINkdm8FdI6zM1R7o4\nizPnznDk9JE675s4KLFVN3d5XdhIFjZaz0IavLisnWU7mbFyBobXDHyw+wNnlyOEuEJNHtEcOXKE\npKQk9uzZg06nY8mSJQQFBXHPPfewf/9+649ue3l5AZCcnMzixYtp164dCxYsYMyYMZcWIyMazTh5\n9iTv7niXxTsXU3m6kimhU4gfGE9P757OLk0IcRG7z+Dj4+MZPnw4iYmJnDt3jhMnTvDiiy/SpUsX\nZs2axdy5c6msrCQlJYW8vDwmTpzI1q1bMZvNjBo1ioKCAtzcar+BkAavHXsr9/L3tX8naVASI3qO\nkG+YCqFhdp3BHz16lPXr15OYmAiAu7s7nTt3Zvny5cTHxwPn/wFYtmwZAJmZmcTFxeHh4YHRaCQw\nMJAtW7ZpYQeWAAARMklEQVQ0dV9alNZnao7Sy7sXD177ICN7jZTmjrwufk+ysNF6Fk26Fk1RURFd\nu3ZlypQp/PjjjwwePJj//d//pby8HF9fXwB8fX0pLy8HoLS0lPDwcOvjDQYDZrO5zm0nJCRgNBoB\n8PLyIjQ0lIiICMAWZksu5+bmOvT5tLycm5urqXpkWRvLF2ilHmcuO6tfZGdnk5aWBmDtl3Vp0ohm\n27Zt3HjjjWzcuJEhQ4bw17/+lU6dOvHGG29QWVlpvZ+Pjw8VFRVMnz6d8PBwJk2aBEBSUhLjxo1j\n/PjxtYuREY3Dnas5x2d5nxHbL1YzV24UQjSOXUc0BoMBg8HAkCFDAJgwYQI7duzAz8+PgwcPAlBW\nVka3bt0A0Ov1FBcXWx9fUlKCXq9vylMLOyo4XMCwJcN4Z8c7nDh7wtnlCCHsrEkN3s/PD39/fwoK\nCgD49ttv6devH1FRUaSnpwOQnp5OTEwMANHR0WRkZGCxWCgqKqKwsJCwsDA77YJ9Xfw2tDWqUTW8\nnvM6f1z8RyaFTGLVn1fR8aqOl9yvLWRxpSQLG8nCRutZNPl68K+//jqTJk3CYrEQEBDAkiVLqK6u\nJjY2lkWLFllPkwQwmUzExsZiMplwd3cnNTVVxgFOcujkIe759B5Onj3JD4k/0Pva3s4uSQjRQuRS\nBW2MpdpCWm4aUwdNdbkrOgoh6ibXohFCiFZKrkVzhbQ+U3MkycJGsrCRLGy0noU0+Faq8lQlM7Nm\n1ntxMCFE6ycjmlbom5+/IWlFEncG30nKqBQ6eHRwdklCiBZUX+9s8lk0QnuOW47z+KrHWfnzStLu\nSGNkr5HOLkkI4UQyormI1mdq9ak6U0XoW6GcqT7Drod22aW5u2oWLUGysJEsbLSehRzBtxKdru7E\n5/d8zgDfAc4uRQihETKDF0IIFyenSbYi8o+gEOJKSIO/iNZnanm/5XHjohvZ8+ueFn8urWfhSJKF\njWRho/UspMG7iBpVw6ubXmV42nCmhE7B1NXk7JKEEBonM3gXUFRZREJmAjWqhrQ70gjwCXB2SUII\nDZEZvIs6W32WyH9HEt07muz4bGnuQogrJg3+IlqbqXm082Dngzt57KbHHH71R61l4UyShY1kYaP1\nLKTBa0jVmao611/jcY2DKxFCtAYyg3eCs9Vn+W7fd+T/lk/+of/7+y2fTld34pcZvzi7PCGEi5Hr\nwTvYuZpz7K3cS5BP0CW/XmWpthD570j6XNsHU1cTfbv0pW/XvnTv2F1+6UoI0WjS4K9QdnY2ERER\njX7cJ3s+Ydevu6xH5Xsr99K9Y3d+fOhHOl3dyf6FOkBTs2iNJAsbycJGK1m0yFk01dXVDBo0iKio\nKAAqKioYPXo0vXv3ZsyYMRw5YrsWeXJyMkFBQQQHB7Nq1armPK1TVJyq4IcDP3DccrzO238o/gEd\nOu7qexf/Hv9vKmZVsHfmXpdt7kII19esI/hXX32V7du3U1VVxfLly5k1axZdunRh1qxZzJ07l8rK\nSlJSUsjLy2PixIls3boVs9nMqFGjKCgowM2t9r8vWjiCv+DD3R/y/YHvrUfkp86eIrhLMO/d+R7B\nXYKdXZ4QQljZ/Qi+pKSEr7/+mqSkJOuGly9fTnx8PADx8fEsW7YMgMzMTOLi4vDw8MBoNBIYGMiW\nLVua+tR2UXWmik3FmzAfM9d5+3HLcfp26cvfh/2d7Q9s5+jso2y5f4s0dyGEy2hyg3/00Ud56aWX\nah2Fl5eX4+vrC4Cvry/l5eUAlJaWYjAYrPczGAyYzXU31payqXgT//juH8RkxBCwIADfl32ZvnI6\n+Yfya93vwnmt9w++nxlDZzA6YDQGT0Ob/PBT6+f4OpJkYSNZ2Gg9iyZdD/7LL7+kW7duDBo0qN4d\n1Ol0DTbF+m5LSEjAaDQC4OXlRWhoqPVDjAvPVd/yF1lfUF1dzYTbJ1xye/GxYvbl7iPUO5S5E+cS\n6BPI+u/XwwGgF9b75+bmXvHztfbl3NxcTdUjy9pYvkAr9Thz2Vn9Ijs7m7S0NABrv6xLk2bwTz/9\nNEuXLsXd3Z3Tp09z7Ngxxo8fz9atW8nOzsbPz4+ysjJGjBjBTz/9REpKCgCzZ88GYOzYsbzwwgsM\nHTq0djGNmMGXHCvh273fsvvX3ewq38Wu8l2crT7L08Oe5vGbHm/sLgkhhMtqsdMk161bx8svv8yK\nFSuYNWsW1157LU8++SQpKSkcOXKk1oesW7ZssX7I+vPPP19yFH9xkTWqhqOnj+J9jfclz7tm7xqW\n5C4hpFsIA3wHEOIbgr6Tvk2OUoQQbVuL/uj2haY6e/ZsYmNjWbRoEUajkY8//hgAk8lEbGwsJpMJ\nd3d3UlNT623EqVtTrUfk//n1P4zsNZIv7vnikvuN7DWyRX5UOlsj57VqgWRhI1nYSBY2Ws+i2Q1+\n+PDhDB8+HAAfHx++/fbbOu/39NNP8/TTT192e7kHcwnpFkJc/zhCfEPwucanuSUKIUSbJN9kFUII\nFyfXgxdCiDZGGvxFLj4VrC2TLGwkCxvJwkbrWUiDF0KIVkpm8EII4eJkBi+EEG2MNPiLaH2m5kiS\nhY1kYSNZ2Gg9C2nwQgjRSskMXgghXJzM4IUQoo2RBn8Rrc/UHEmysJEsbCQLG61nIQ1eCCFaKZnB\nCyGEi5MZvBBCtDHS4C+i9ZmaI0kWNpKFjWRho/UspMELIUQrJTN4IYRwcTKDF0KINkYa/EW0PlNz\nJMnCRrKwkSxstJ5Fkxp8cXExI0aMoF+/fvTv358FCxYAUFFRwejRo+nduzdjxozhyJEj1sckJycT\nFBREcHAwq1atsk/1LSA3N9fZJWiGZGEjWdhIFjZaz6JJDd7Dw4PXXnuNPXv2sHnzZv71r3+Rn59P\nSkoKo0ePpqCggJEjR5KSkgJAXl4eH330EXl5eWRlZTFt2jRqamrsuiP28vt/lNo6ycJGsrCRLGy0\nnkWTGryfnx+hoaEAdOzYkb59+2I2m1m+fDnx8fEAxMfHs2zZMgAyMzOJi4vDw8MDo9FIYGAgW7Zs\nsdMu2Ne+ffucXQKgjbd+koWNZGEjWdhoJYv6NHsGv2/fPnbu3MnQoUMpLy/H19cXAF9fX8rLywEo\nLS3FYDBYH2MwGDCbzc196hahlbdcWnjxShY2koWNZGGjlSzqpZqhqqpKXX/99eqLL75QSinl5eVV\n63Zvb2+llFKPPPKIev/9963rp06dqj777LNLtgfIn/zJn/zJXxP+6uJOE509e5a77rqLP//5z8TE\nxADnj9oPHjyIn58fZWVldOvWDQC9Xk9xcbH1sSUlJej1+ku2KefACyGE/TRpRKOUYurUqZhMJv76\n179a10dHR5Oeng5Aenq6tfFHR0eTkZGBxWKhqKiIwsJCwsLC7FC+EEKI+jTpm6wbNmzglltuYcCA\nAeh0OuD8aZBhYWHExsZy4MABjEYjH3/8MV5eXgDMmTOHxYsX4+7uzvz587ntttvsuydCCCFqa84M\n3hUcOHBARUREKJPJpPr166fmz5+vlFLq8OHDatSoUSooKEiNHj1aVVZWWh8zZ84cFRgYqPr06aO+\n+eabS7YZFRWl+vfv77B9sBd7ZnHmzBl1//33q969e6vg4OA6P1PRMntmsXjxYtW/f381YMAANXbs\nWHXo0CGH709zNDaLw4cPq4iICNWxY0f1yCOP1NrWtm3bVP/+/VVgYKCaMWOGw/elueyVxcmTJ9W4\nceNUcHCw6tevn5o9e7ZT9qfVN/iysjK1c+dOpdT5D4V79+6t8vLy1BNPPKHmzp2rlFIqJSVFPfnk\nk0oppfbs2aMGDhyoLBaLKioqUgEBAaq6utq6vc8++0xNnDhRhYSEOH5nmskeWdTU1CillPrHP/6h\nnn32Weu2Xa2p2SuLM2fOKB8fH3X48GGllFKzZs1Szz//vHN2qokam8WJEyfUhg0b1FtvvXVJgx8y\nZIjKyclRSikVGRmpVq5c6cA9aT57ZXHy5EmVnZ2tlFLKYrGoYcOGOSWLVt/gL3bHHXeo1atXqz59\n+qiDBw8qpc7/T+3Tp49S6vxRWkpKivX+t912m9q0aZNS6vz/8Jtvvlnl5eW55BH8xZqSxebNm5VS\nSvn7+6uTJ086vugW0tQsqqurVUBAgNq/f7+qqalRDz30kHrnnXecsg/2crksLliyZEmtplZaWqqC\ng4Otyx9++KF68MEHHVN0C2lqFhebOXOmevfdd1u01rq0qWvRNPWc/dLSUgCeffZZHn/8cTp06OD4\n4u2sOd9fuPDtvWeeeYbBgwcTGxvLr7/+6vidsJOmZlFSUoKbmxvz58+nf//+6PV68vPzSUxMdMp+\n2MOVZHHBhc/fLjCbzbUy0uv1mv2+y5VoTha/d+TIEVasWMHIkSNbtN66tJkGf/z4ce666y7mz59P\np06dat2m0+ka/B+klCI3N5e9e/dyxx13uPzpnM3JAuDcuXOUlJTwxz/+ke3bt3PjjTfy+OOPt2TJ\nLaY5Weh0Oo4dO8aMGTP48ccfKS0tJSQkhOTk5JYuu0U093XRmtgri3PnzhEXF8fMmTMxGo0tUGnD\n2kSDb+icfeCy5+wbDAY2b97Mtm3b6NmzJ8OGDaOgoIBbb73V8TvTTM3NQq/Xc+2119KhQwfGjx8P\nwIQJE9ixY4eD96T57JFFfn4+PXv2pGfPngDcfffdbNy40cF70nyNyaI+er2ekpIS63J933fROntk\nccEDDzxAnz59mDFjRovV25BW3+CVnc7Zf+ihhzCbzRQVFbFhwwZ69+7N2rVrnbJPTWWvLHQ6HVFR\nUXz33XcArFmzhn79+jl+h5rBXln06tWLn376iUOHDgGwevVqTCaT43eoGRqbxe8f93vdu3fH09OT\nnJwclFIsXbr0ksdonb2ygPMjzGPHjvHaa6+1bNENcfjU38HWr1+vdDqdGjhwoAoNDVWhoaFq5cqV\n6vDhw2rkyJF1ng734osvqoCAANWnTx+VlZV1yTaLiopc8iwae2axf/9+dcstt6gBAwaoUaNGqeLi\nYmfsUpPZM4v09HTraZLR0dGqoqLCGbvUZE3JokePHsrHx0d17NhRGQwGlZ+fr5SynSYZEBCgpk+f\n7qxdajJ7ZVFcXKx0Op0ymUzW7SxatMjh+6Opn+wTQghhP61+RCOEEG2VNHghhGilpMELIUQrJQ1e\nCCFaKWnwQvyf559/nldeeaXe2zMzM8nPz3dgRUI0jzR4If7P5b6d+MUXX5CXl+egaoRoPjlNUrRp\nL774Iu+99x7dunXD39+fwYMH07lzZxYuXIjFYiEwMJClS5eyc+dOoqKi6Ny5M507d+bzzz+npqaG\nRx55hN9++40OHTrwzjvv0KdPH2fvkhA2Dj/zXgiN2LZtmwoJCVGnTp1Sx44dU4GBgeqVV16xXvpX\nKaWeeeYZ9frrryullEpISKh13ftbb71VFRYWKqWU2rx5s7r11lsduwNCXEaTf5NVCFe3fv16xo8f\nT/v27Wnfvj3R0dEopdi9ezfPPPMMR48e5fjx44wdO9b6GPV/b3iPHz/Opk2buPvuu623WSwWh++D\nEA2RBi/aLJ1OV+c1RKZMmUJmZiYhISGkp6eTnZ1d6zEANTU1eHl5sXPnTkeVK0SjyYesos265ZZb\nWLZsGadPn6aqqooVK1YAUFVVhZ+fH2fPnuX999+3NvVOnTpx7NgxADw9PenZsyeffvopcP7Ifteu\nXc7ZESHqIR+yijZtzpw5pKen061bN3r06MH1119Phw4dmDdvHl27dmXo0KEcP36cxYsXs3HjRu6/\n/37at2/Pp59+ik6n4+GHH6asrIyzZ88SFxfHM8884+xdEsJKGrwQQrRSMqIRQohWShq8EEK0UtLg\nhRCilZIGL4QQrZQ0eCGEaKWkwQshRCv1/wM0O1pIRS1iBAAAAABJRU5ErkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0x3b97f90>"
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "ts_res = ts.resample(\"A\", how=[\"mean\", np.max, np.min])\nts_res.plot(subplots=True)\nts_res.plot()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 5,
       "text": "<matplotlib.axes.AxesSubplot at 0x4daf710>"
      },
      {
       "output_type": "display_data",
       "png": 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Vz8yZM5GamooTJ07gyZMnePLkCaKjo5GcnAwAqKiogJaWFgYOHIioqCicOnWq3S920/2F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\nQmCC\n",
       "text": "<matplotlib.figure.Figure at 0x4769350>"
      },
      {
       "output_type": "display_data",
       "png": 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VU/dPoevC0BD44w9gxw5WD3DoUJbMXg1wI8/hcAThR8mP6GrQFZ92/LRS/aSl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\n",
       "text": "<matplotlib.figure.Figure at 0x4760ad0>"
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "iris = pd.read_csv(\"iris.csv\")\npd.tools.plotting.radviz(iris, \"name\")",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 8,
       "text": "<matplotlib.axes.AxesSubplot at 0x5031090>"
      },
      {
       "output_type": "display_data",
       "png": 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hFM6fP0/nzp1ZsWIFAwYMyPN6ebgW33//fWJjYwkKCjJ0KIX2ojcxqfI8I0kv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\nRU5ErkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0x476ba50>"
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "iris.head()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>SepalLength</th>\n      <th>SepalWidth</th>\n      <th>PetalLength</th>\n      <th>PetalWidth</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> setosa</td>\n      <td> 5.1</td>\n      <td> 3.5</td>\n      <td> 1.4</td>\n      <td> 0.2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> setosa</td>\n      <td> 4.9</td>\n      <td> 3.0</td>\n      <td> 1.4</td>\n      <td> 0.2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> setosa</td>\n      <td> 4.7</td>\n      <td> 3.2</td>\n      <td> 1.3</td>\n      <td> 0.2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> setosa</td>\n      <td> 4.6</td>\n      <td> 3.1</td>\n      <td> 1.5</td>\n      <td> 0.2</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> setosa</td>\n      <td> 5.0</td>\n      <td> 3.6</td>\n      <td> 1.4</td>\n      <td> 0.2</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 9,
       "text": "     name  SepalLength  SepalWidth  PetalLength  PetalWidth\n0  setosa          5.1         3.5          1.4         0.2\n1  setosa          4.9         3.0          1.4         0.2\n2  setosa          4.7         3.2          1.3         0.2\n3  setosa          4.6         3.1          1.5         0.2\n4  setosa          5.0         3.6          1.4         0.2"
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "iris.describe()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>SepalLength</th>\n      <th>SepalWidth</th>\n      <th>PetalLength</th>\n      <th>PetalWidth</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td> 150.000000</td>\n      <td> 150.000000</td>\n      <td> 150.000000</td>\n      <td> 150.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>   5.843333</td>\n      <td>   3.057333</td>\n      <td>   3.758000</td>\n      <td>   1.199333</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>   0.828066</td>\n      <td>   0.435866</td>\n      <td>   1.765298</td>\n      <td>   0.762238</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>   4.300000</td>\n      <td>   2.000000</td>\n      <td>   1.000000</td>\n      <td>   0.100000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>   5.100000</td>\n      <td>   2.800000</td>\n      <td>   1.600000</td>\n      <td>   0.300000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>   5.800000</td>\n      <td>   3.000000</td>\n      <td>   4.350000</td>\n      <td>   1.300000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>   6.400000</td>\n      <td>   3.300000</td>\n      <td>   5.100000</td>\n      <td>   1.800000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>   7.900000</td>\n      <td>   4.400000</td>\n      <td>   6.900000</td>\n      <td>   2.500000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 10,
       "text": "       SepalLength  SepalWidth  PetalLength  PetalWidth\ncount   150.000000  150.000000   150.000000  150.000000\nmean      5.843333    3.057333     3.758000    1.199333\nstd       0.828066    0.435866     1.765298    0.762238\nmin       4.300000    2.000000     1.000000    0.100000\n25%       5.100000    2.800000     1.600000    0.300000\n50%       5.800000    3.000000     4.350000    1.300000\n75%       6.400000    3.300000     5.100000    1.800000\nmax       7.900000    4.400000     6.900000    2.500000"
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "iris.groupby(\"name\").sum()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>SepalLength</th>\n      <th>SepalWidth</th>\n      <th>PetalLength</th>\n      <th>PetalWidth</th>\n    </tr>\n    <tr>\n      <th>name</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>setosa</th>\n      <td> 250.3</td>\n      <td> 171.4</td>\n      <td>  73.1</td>\n      <td>  12.3</td>\n    </tr>\n    <tr>\n      <th>versicolor</th>\n      <td> 296.8</td>\n      <td> 138.5</td>\n      <td> 213.0</td>\n      <td>  66.3</td>\n    </tr>\n    <tr>\n      <th>virginica</th>\n      <td> 329.4</td>\n      <td> 148.7</td>\n      <td> 277.6</td>\n      <td> 101.3</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 11,
       "text": "            SepalLength  SepalWidth  PetalLength  PetalWidth\nname                                                        \nsetosa            250.3       171.4         73.1        12.3\nversicolor        296.8       138.5        213.0        66.3\nvirginica         329.4       148.7        277.6       101.3"
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "iris.groupby(\"name\").max()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>SepalLength</th>\n      <th>SepalWidth</th>\n      <th>PetalLength</th>\n      <th>PetalWidth</th>\n    </tr>\n    <tr>\n      <th>name</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>setosa</th>\n      <td> 5.8</td>\n      <td> 4.4</td>\n      <td> 1.9</td>\n      <td> 0.6</td>\n    </tr>\n    <tr>\n      <th>versicolor</th>\n      <td> 7.0</td>\n      <td> 3.4</td>\n      <td> 5.1</td>\n      <td> 1.8</td>\n    </tr>\n    <tr>\n      <th>virginica</th>\n      <td> 7.9</td>\n      <td> 3.8</td>\n      <td> 6.9</td>\n      <td> 2.5</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 12,
       "text": "            SepalLength  SepalWidth  PetalLength  PetalWidth\nname                                                        \nsetosa              5.8         4.4          1.9         0.6\nversicolor          7.0         3.4          5.1         1.8\nvirginica           7.9         3.8          6.9         2.5"
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "iris.groupby(\"name\").min()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>SepalLength</th>\n      <th>SepalWidth</th>\n      <th>PetalLength</th>\n      <th>PetalWidth</th>\n    </tr>\n    <tr>\n      <th>name</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>setosa</th>\n      <td> 4.3</td>\n      <td> 2.3</td>\n      <td> 1.0</td>\n      <td> 0.1</td>\n    </tr>\n    <tr>\n      <th>versicolor</th>\n      <td> 4.9</td>\n      <td> 2.0</td>\n      <td> 3.0</td>\n      <td> 1.0</td>\n    </tr>\n    <tr>\n      <th>virginica</th>\n      <td> 4.9</td>\n      <td> 2.2</td>\n      <td> 4.5</td>\n      <td> 1.4</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 13,
       "text": "            SepalLength  SepalWidth  PetalLength  PetalWidth\nname                                                        \nsetosa              4.3         2.3          1.0         0.1\nversicolor          4.9         2.0          3.0         1.0\nvirginica           4.9         2.2          4.5         1.4"
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "iris.groupby(\"name\").describe()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>SepalLength</th>\n      <th>SepalWidth</th>\n      <th>PetalLength</th>\n      <th>PetalWidth</th>\n    </tr>\n    <tr>\n      <th>name</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"8\" valign=\"top\">setosa</th>\n      <th>count</th>\n      <td> 50.000000</td>\n      <td> 50.000000</td>\n      <td> 50.000000</td>\n      <td> 50.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>  5.006000</td>\n      <td>  3.428000</td>\n      <td>  1.462000</td>\n      <td>  0.246000</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>  0.352490</td>\n      <td>  0.379064</td>\n      <td>  0.173664</td>\n      <td>  0.105386</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>  4.300000</td>\n      <td>  2.300000</td>\n      <td>  1.000000</td>\n      <td>  0.100000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>  4.800000</td>\n      <td>  3.200000</td>\n      <td>  1.400000</td>\n      <td>  0.200000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>  5.000000</td>\n      <td>  3.400000</td>\n      <td>  1.500000</td>\n      <td>  0.200000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>  5.200000</td>\n      <td>  3.675000</td>\n      <td>  1.575000</td>\n      <td>  0.300000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>  5.800000</td>\n      <td>  4.400000</td>\n      <td>  1.900000</td>\n      <td>  0.600000</td>\n    </tr>\n    <tr>\n      <th rowspan=\"8\" valign=\"top\">versicolor</th>\n      <th>count</th>\n      <td> 50.000000</td>\n      <td> 50.000000</td>\n      <td> 50.000000</td>\n      <td> 50.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>  5.936000</td>\n      <td>  2.770000</td>\n      <td>  4.260000</td>\n      <td>  1.326000</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>  0.516171</td>\n      <td>  0.313798</td>\n      <td>  0.469911</td>\n      <td>  0.197753</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>  4.900000</td>\n      <td>  2.000000</td>\n      <td>  3.000000</td>\n      <td>  1.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>  5.600000</td>\n      <td>  2.525000</td>\n      <td>  4.000000</td>\n      <td>  1.200000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>  5.900000</td>\n      <td>  2.800000</td>\n      <td>  4.350000</td>\n      <td>  1.300000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>  6.300000</td>\n      <td>  3.000000</td>\n      <td>  4.600000</td>\n      <td>  1.500000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>  7.000000</td>\n      <td>  3.400000</td>\n      <td>  5.100000</td>\n      <td>  1.800000</td>\n    </tr>\n    <tr>\n      <th rowspan=\"8\" valign=\"top\">virginica</th>\n      <th>count</th>\n      <td> 50.000000</td>\n      <td> 50.000000</td>\n      <td> 50.000000</td>\n      <td> 50.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>  6.588000</td>\n      <td>  2.974000</td>\n      <td>  5.552000</td>\n      <td>  2.026000</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>  0.635880</td>\n      <td>  0.322497</td>\n      <td>  0.551895</td>\n      <td>  0.274650</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>  4.900000</td>\n      <td>  2.200000</td>\n      <td>  4.500000</td>\n      <td>  1.400000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>  6.225000</td>\n      <td>  2.800000</td>\n      <td>  5.100000</td>\n      <td>  1.800000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>  6.500000</td>\n      <td>  3.000000</td>\n      <td>  5.550000</td>\n      <td>  2.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>  6.900000</td>\n      <td>  3.175000</td>\n      <td>  5.875000</td>\n      <td>  2.300000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>  7.900000</td>\n      <td>  3.800000</td>\n      <td>  6.900000</td>\n      <td>  2.500000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 14,
       "text": "                  SepalLength  SepalWidth  PetalLength  PetalWidth\nname                                                              \nsetosa     count    50.000000   50.000000    50.000000   50.000000\n           mean      5.006000    3.428000     1.462000    0.246000\n           std       0.352490    0.379064     0.173664    0.105386\n           min       4.300000    2.300000     1.000000    0.100000\n           25%       4.800000    3.200000     1.400000    0.200000\n           50%       5.000000    3.400000     1.500000    0.200000\n           75%       5.200000    3.675000     1.575000    0.300000\n           max       5.800000    4.400000     1.900000    0.600000\nversicolor count    50.000000   50.000000    50.000000   50.000000\n           mean      5.936000    2.770000     4.260000    1.326000\n           std       0.516171    0.313798     0.469911    0.197753\n           min       4.900000    2.000000     3.000000    1.000000\n           25%       5.600000    2.525000     4.000000    1.200000\n           50%       5.900000    2.800000     4.350000    1.300000\n           75%       6.300000    3.000000     4.600000    1.500000\n           max       7.000000    3.400000     5.100000    1.800000\nvirginica  count    50.000000   50.000000    50.000000   50.000000\n           mean      6.588000    2.974000     5.552000    2.026000\n           std       0.635880    0.322497     0.551895    0.274650\n           min       4.900000    2.200000     4.500000    1.400000\n           25%       6.225000    2.800000     5.100000    1.800000\n           50%       6.500000    3.000000     5.550000    2.000000\n           75%       6.900000    3.175000     5.875000    2.300000\n           max       7.900000    3.800000     6.900000    2.500000"
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "iris[\"SepalLength\"].corr(iris[\"PetalLength\"])",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 15,
       "text": "0.87175377588658287"
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "iris.corr()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>SepalLength</th>\n      <th>SepalWidth</th>\n      <th>PetalLength</th>\n      <th>PetalWidth</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>SepalLength</th>\n      <td> 1.000000</td>\n      <td>-0.117570</td>\n      <td> 0.871754</td>\n      <td> 0.817941</td>\n    </tr>\n    <tr>\n      <th>SepalWidth</th>\n      <td>-0.117570</td>\n      <td> 1.000000</td>\n      <td>-0.428440</td>\n      <td>-0.366126</td>\n    </tr>\n    <tr>\n      <th>PetalLength</th>\n      <td> 0.871754</td>\n      <td>-0.428440</td>\n      <td> 1.000000</td>\n      <td> 0.962865</td>\n    </tr>\n    <tr>\n      <th>PetalWidth</th>\n      <td> 0.817941</td>\n      <td>-0.366126</td>\n      <td> 0.962865</td>\n      <td> 1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 16,
       "text": "             SepalLength  SepalWidth  PetalLength  PetalWidth\nSepalLength     1.000000   -0.117570     0.871754    0.817941\nSepalWidth     -0.117570    1.000000    -0.428440   -0.366126\nPetalLength     0.871754   -0.428440     1.000000    0.962865\nPetalWidth      0.817941   -0.366126     0.962865    1.000000"
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}